About: Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks
Creator	Ahmed, Ghufran Ghafoor, Mubeen Munir, Kamran Saqib, Madeeha Tariq, Syed »more»
source	PMC
abstract	Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.
has issue date	2020-01-28(xsd:dateTime)
bibo:doi	10.1109/access.2020.2970023
bibo:pmid	32391238
has license	cc-by
sha1sum (hex)	4a8e14467dda8a59309ecbd9ddb86cd28cae17d6
schema:url	https://doi.org/10.1109/access.2020.2970023
resource representing a document's title	Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks
has PubMed Central identifier	PMC7176037
has PubMed identifier	32391238
schema:publication	IEEE Access
resource representing a document's body	covid:4a8e14467dda8a59309ecbd9ddb86cd28cae17d6#body_text
is schema:about of	named entity 'presents' named entity 'GHz' named entity 'CNN' named entity 'deals' named entity 'convolutional neural network' »more»

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